The design of adaptive observations strategies must account for the
particular properties of the data assimilation method. A new adjoint
sensitivity approach to the targeted observations problem is proposed
in the context of four-dimensional variational data assimilation
(4D-Var). The method is based on a periodic update of the adjoint
sensitivity field that takes into account the interaction between time
dis- tributed adaptive and routine observations. Information provided
by all previously located observations is used to identify best
locations for new targeted observations. Adaptive observations at
distinct instants in time are selected in a sequential man- ner such
that the method is only suboptimal. The selection algorithm proceeds
backward in time and requires only one additional adjoint model
integration in the assimilation window. Therefore, the method is very
efficient and is suitable for practical applications. A comparative
performance analysis is presented using the traditional adjoint
sensitivity method as well as the total energy singular vectors
technique as alternative adaptive strategies. Numerical experiments
are performed in the twin experiments framework using a
two-dimensional global shallow water model in spherical coordinates
and an explicit Turkel-Zwas discretization scheme. Data from a NASA
500mb analysis valid for 00Z 16 Mar 2001 6h obtained with the GEOS-3
model was used to specify the geopotential height at the initial time
and the initial velocities were obtained from a geostrophic balance.
Numerical results show that the new adaptive observations approach
outperforms traditional target- ing methods in terms of forecast error
reduction at the verification time and its implementation is feasible
for large scale atmospheric models.